Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
fairret: a Framework for Differentiable Fairness Regularization Terms
Authors: Maarten Buyl, MaryBeth Defrance, Tijl De Bie
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a Py Torch implementation of the FAIRRET framework. We visualize the FAIRRETs gradients and evaluate their empirical performance in enforcing fairness notions compared to baselines. |
| Researcher Affiliation | Academia | Maarten Buyl Ghent University EMAIL Mary Beth Defrance Ghent University EMAIL Tijl De Bie Ghent University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. Appendix E provides 'Code Use Examples' which are snippets of PyTorch code, not pseudocode. |
| Open Source Code | Yes | The framework is available as a package at https://github.com/aida-ugent/fairret. |
| Open Datasets | Yes | Experiments were conducted on the Bank (Moro et al., 2014), Credit Card (Yeh & hui Lien, 2009), Law School4, and ACSIncome (Ding et al., 2021) datasets. |
| Dataset Splits | Yes | To find these hyperparameters, we took the 80%/20% train/test split already generated for each seed, and further divided the train set into a smaller train set and a validation set with relative sizes 80% and 20% respectively. |
| Hardware Specification | Yes | All experiments in Sec. 4 were conducted on an internal server equipped with a 12 Core Intel(R) Xeon(R) Gold processor and 256 GB of RAM. |
| Software Dependencies | No | The paper mentions 'Py Torch', 'cvxpy', and 'Adam optimizer' but does not specify their version numbers or other key software dependencies with version information required for reproducibility. |
| Experiment Setup | Yes | The classifier βwas a fully connected neural net with hidden layers of sizes [256, 128, 32] followed by a sigmoid... optimized with the Adam optimizer implementation of Py Torch, with a learning rate of 0.001 and a batch size of 4096. The loss was minimized over 100 epochs, with π= 0 for the first 20 to avoid constraining βbefore it learns anything. |